Overview

Dataset statistics

Number of variables19
Number of observations12348
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory181.4 B

Variable types

Numeric16
Categorical3

Alerts

TotalSteps is highly overall correlated with TotalDistance and 6 other fieldsHigh correlation
TotalDistance is highly overall correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly overall correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly overall correlated with TotalSteps and 6 other fieldsHigh correlation
FairlyActiveMinutes is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly overall correlated with LightActiveDistanceHigh correlation
Calories is highly overall correlated with TotalDistance and 2 other fieldsHigh correlation
TotalMinutesAsleep is highly overall correlated with TotalTimeInBedHigh correlation
TotalTimeInBed is highly overall correlated with TotalMinutesAsleepHigh correlation
TotalSleepRecords is highly imbalanced (65.2%)Imbalance
TotalSteps has 611 (4.9%) zerosZeros
TotalDistance has 614 (5.0%) zerosZeros
TrackerDistance has 614 (5.0%) zerosZeros
LoggedActivitiesDistance has 11732 (95.0%) zerosZeros
VeryActiveDistance has 4658 (37.7%) zerosZeros
ModeratelyActiveDistance has 4334 (35.1%) zerosZeros
LightActiveDistance has 663 (5.4%) zerosZeros
SedentaryActiveDistance has 12212 (98.9%) zerosZeros
VeryActiveMinutes has 4658 (37.7%) zerosZeros
FairlyActiveMinutes has 4319 (35.0%) zerosZeros
LightlyActiveMinutes has 660 (5.3%) zerosZeros

Reproduction

Analysis started2023-01-21 20:09:13.775437
Analysis finished2023-01-21 20:09:32.486971
Duration18.71 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0210158 × 109
Minimum1.5039604 × 109
Maximum8.7920097 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:32.533349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5039604 × 109
5-th percentile1.5039604 × 109
Q13.9773337 × 109
median4.7029217 × 109
Q36.9621811 × 109
95-th percentile8.3785632 × 109
Maximum8.7920097 × 109
Range7.2880493 × 109
Interquartile range (IQR)2.9848474 × 109

Descriptive statistics

Standard deviation2.0483114 × 109
Coefficient of variation (CV)0.40794762
Kurtosis-0.73439593
Mean5.0210158 × 109
Median Absolute Deviation (MAD)8.7422863 × 108
Skewness0.0074911938
Sum6.1999504 × 1013
Variance4.1955798 × 1018
MonotonicityIncreasing
2023-01-21T12:09:32.604340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8378563200 961
 
7.8%
6962181067 961
 
7.8%
5553957443 961
 
7.8%
2026352035 868
 
7.0%
4445114986 868
 
7.0%
3977333714 840
 
6.8%
4702921684 837
 
6.8%
4319703577 806
 
6.5%
5577150313 780
 
6.3%
1503960366 775
 
6.3%
Other values (14) 3691
29.9%
ValueCountFrequency (%)
1503960366 775
6.3%
1644430081 120
 
1.0%
1844505072 93
 
0.8%
1927972279 155
 
1.3%
2026352035 868
7.0%
2320127002 31
 
0.3%
2347167796 270
 
2.2%
3977333714 840
6.8%
4020332650 248
 
2.0%
4319703577 806
6.5%
ValueCountFrequency (%)
8792009665 435
3.5%
8378563200 961
7.8%
8053475328 93
 
0.8%
7086361926 744
6.0%
7007744171 52
 
0.4%
6962181067 961
7.8%
6775888955 78
 
0.6%
6117666160 504
4.1%
5577150313 780
6.3%
5553957443 961
7.8%

ActivityDate
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size192.9 KiB
4/12/2016
 
410
4/22/2016
 
410
4/29/2016
 
410
4/28/2016
 
410
4/13/2016
 
410
Other values (26)
10298 

Length

Max length9
Median length9
Mean length8.712909
Min length8

Characters and Unicode

Total characters107587
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/12/2016
2nd row4/12/2016
3rd row4/12/2016
4th row4/12/2016
5th row4/12/2016

Common Values

ValueCountFrequency (%)
4/12/2016 410
 
3.3%
4/22/2016 410
 
3.3%
4/29/2016 410
 
3.3%
4/28/2016 410
 
3.3%
4/13/2016 410
 
3.3%
4/26/2016 410
 
3.3%
4/25/2016 410
 
3.3%
4/24/2016 410
 
3.3%
4/23/2016 410
 
3.3%
4/27/2016 410
 
3.3%
Other values (21) 8248
66.8%

Length

2023-01-21T12:09:32.675368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/12/2016 410
 
3.3%
4/21/2016 410
 
3.3%
4/20/2016 410
 
3.3%
4/14/2016 410
 
3.3%
4/22/2016 410
 
3.3%
4/16/2016 410
 
3.3%
4/17/2016 410
 
3.3%
4/18/2016 410
 
3.3%
4/19/2016 410
 
3.3%
4/15/2016 410
 
3.3%
Other values (21) 8248
66.8%

Most occurring characters

ValueCountFrequency (%)
/ 24696
23.0%
2 17962
16.7%
1 17818
16.6%
6 13563
12.6%
0 13525
12.6%
4 8990
 
8.4%
5 5788
 
5.4%
3 1610
 
1.5%
7 1215
 
1.1%
9 1210
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82891
77.0%
Other Punctuation 24696
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17962
21.7%
1 17818
21.5%
6 13563
16.4%
0 13525
16.3%
4 8990
10.8%
5 5788
 
7.0%
3 1610
 
1.9%
7 1215
 
1.5%
9 1210
 
1.5%
8 1210
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 24696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107587
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 24696
23.0%
2 17962
16.7%
1 17818
16.6%
6 13563
12.6%
0 13525
12.6%
4 8990
 
8.4%
5 5788
 
5.4%
3 1610
 
1.5%
7 1215
 
1.1%
9 1210
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 24696
23.0%
2 17962
16.7%
1 17818
16.6%
6 13563
12.6%
0 13525
12.6%
4 8990
 
8.4%
5 5788
 
5.4%
3 1610
 
1.5%
7 1215
 
1.1%
9 1210
 
1.1%

TotalSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct635
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8107.8903
Minimum0
Maximum22988
Zeros611
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:32.741931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q14660
median8585
Q311317
95-th percentile15036
Maximum22988
Range22988
Interquartile range (IQR)6657

Descriptive statistics

Standard deviation4482.7866
Coefficient of variation (CV)0.55289187
Kurtosis-0.44079553
Mean8107.8903
Median Absolute Deviation (MAD)3353
Skewness0.018722161
Sum1.0011623 × 108
Variance20095376
MonotonicityNot monotonic
2023-01-21T12:09:32.813169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
4.9%
12764 56
 
0.5%
9105 55
 
0.4%
5232 33
 
0.3%
10725 32
 
0.3%
4363 32
 
0.3%
14461 31
 
0.3%
13070 31
 
0.3%
10199 31
 
0.3%
7626 31
 
0.3%
Other values (625) 11405
92.4%
ValueCountFrequency (%)
0 611
4.9%
4 3
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
16 8
 
0.1%
17 26
 
0.2%
29 26
 
0.2%
31 24
 
0.2%
42 15
 
0.1%
44 3
 
< 0.1%
ValueCountFrequency (%)
22988 3
 
< 0.1%
22770 23
0.2%
22359 3
 
< 0.1%
22244 15
0.1%
22026 3
 
< 0.1%
20669 3
 
< 0.1%
20500 3
 
< 0.1%
20159 3
 
< 0.1%
20067 2
 
< 0.1%
20031 31
0.3%

TotalDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct499
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7220408
Minimum0
Maximum17.950001
Zeros614
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:32.885056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0099999998
Q13.1600001
median6.1199999
Q37.9200001
95-th percentile10.71
Maximum17.950001
Range17.950001
Interquartile range (IQR)4.76

Descriptive statistics

Standard deviation3.2426335
Coefficient of variation (CV)0.56669178
Kurtosis-0.24447869
Mean5.7220408
Median Absolute Deviation (MAD)2.3799999
Skewness0.11481085
Sum70655.76
Variance10.514672
MonotonicityNot monotonic
2023-01-21T12:09:32.956164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 614
 
5.0%
6.710000038 114
 
0.9%
6.820000172 83
 
0.7%
7.099999905 78
 
0.6%
2.559999943 78
 
0.6%
2.599999905 70
 
0.6%
3.619999886 70
 
0.6%
0.009999999776 64
 
0.5%
2.769999981 62
 
0.5%
7.630000114 62
 
0.5%
Other values (489) 11053
89.5%
ValueCountFrequency (%)
0 614
5.0%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 18
 
0.1%
0.03999999911 8
 
0.1%
0.07999999821 8
 
0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 3
 
< 0.1%
ValueCountFrequency (%)
17.95000076 3
 
< 0.1%
17.64999962 3
 
< 0.1%
17.54000092 23
0.2%
17.19000053 3
 
< 0.1%
16.23999977 3
 
< 0.1%
15.97000027 3
 
< 0.1%
15.68999958 3
 
< 0.1%
15.67000008 3
 
< 0.1%
15.07999992 15
0.1%
15.01000023 18
0.1%

TrackerDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct497
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7150178
Minimum0
Maximum17.950001
Zeros614
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.124329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0099999998
Q13.1600001
median6.1199999
Q37.8800001
95-th percentile10.71
Maximum17.950001
Range17.950001
Interquartile range (IQR)4.72

Descriptive statistics

Standard deviation3.2349602
Coefficient of variation (CV)0.56604552
Kurtosis-0.23299746
Mean5.7150178
Median Absolute Deviation (MAD)2.3599997
Skewness0.11354228
Sum70569.04
Variance10.464968
MonotonicityNot monotonic
2023-01-21T12:09:33.194780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 614
 
5.0%
6.710000038 114
 
0.9%
6.820000172 83
 
0.7%
2.559999943 78
 
0.6%
7.099999905 78
 
0.6%
2.599999905 70
 
0.6%
3.619999886 70
 
0.6%
0.009999999776 64
 
0.5%
7.630000114 62
 
0.5%
9.079999924 62
 
0.5%
Other values (487) 11053
89.5%
ValueCountFrequency (%)
0 614
5.0%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 18
 
0.1%
0.03999999911 8
 
0.1%
0.07999999821 8
 
0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 3
 
< 0.1%
ValueCountFrequency (%)
17.95000076 3
 
< 0.1%
17.64999962 3
 
< 0.1%
17.54000092 23
0.2%
17.19000053 3
 
< 0.1%
16.23999977 3
 
< 0.1%
15.97000027 3
 
< 0.1%
15.68999958 3
 
< 0.1%
15.67000008 3
 
< 0.1%
15.07999992 15
0.1%
15.01000023 18
0.1%

LoggedActivitiesDistance
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12145714
Minimum0
Maximum4.942142
Zeros11732
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.259388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.942142
Range4.942142
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55017825
Coefficient of variation (CV)4.5298138
Kurtosis24.672873
Mean0.12145714
Median Absolute Deviation (MAD)0
Skewness4.8172576
Sum1499.7528
Variance0.30269611
MonotonicityNot monotonic
2023-01-21T12:09:33.315388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 11732
95.0%
2.092147112 279
 
2.3%
2.253081083 217
 
1.8%
4.081692219 31
 
0.3%
3.167821884 31
 
0.3%
2.785175085 31
 
0.3%
1.959596038 3
 
< 0.1%
4.885604858 2
 
< 0.1%
4.878232002 2
 
< 0.1%
4.912367821 2
 
< 0.1%
Other values (9) 18
 
0.1%
ValueCountFrequency (%)
0 11732
95.0%
1.959596038 3
 
< 0.1%
2.092147112 279
 
2.3%
2.253081083 217
 
1.8%
2.785175085 31
 
0.3%
2.832325935 2
 
< 0.1%
3.167821884 31
 
0.3%
3.285414934 2
 
< 0.1%
4.081692219 31
 
0.3%
4.851306915 2
 
< 0.1%
ValueCountFrequency (%)
4.94214201 2
< 0.1%
4.930550098 2
< 0.1%
4.924840927 2
< 0.1%
4.912367821 2
< 0.1%
4.911146164 2
< 0.1%
4.885604858 2
< 0.1%
4.878232002 2
< 0.1%
4.869782925 2
< 0.1%
4.861792088 2
< 0.1%
4.851306915 2
< 0.1%

VeryActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3974976
Minimum0
Maximum13.4
Zeros4658
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.381186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.52999997
Q32.3099999
95-th percentile5.29
Maximum13.4
Range13.4
Interquartile range (IQR)2.3099999

Descriptive statistics

Standard deviation1.9107099
Coefficient of variation (CV)1.3672367
Kurtosis4.2454423
Mean1.3974976
Median Absolute Deviation (MAD)0.52999997
Skewness1.8444789
Sum17256.3
Variance3.6508125
MonotonicityNot monotonic
2023-01-21T12:09:33.457727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4658
37.7%
0.0700000003 132
 
1.1%
2.029999971 98
 
0.8%
1.370000005 87
 
0.7%
1.059999943 83
 
0.7%
0.3199999928 81
 
0.7%
0.05999999866 79
 
0.6%
2.789999962 78
 
0.6%
0.3700000048 78
 
0.6%
0.3300000131 77
 
0.6%
Other values (266) 6897
55.9%
ValueCountFrequency (%)
0 4658
37.7%
0.01999999955 20
 
0.2%
0.03999999911 15
 
0.1%
0.05000000075 20
 
0.2%
0.05999999866 79
 
0.6%
0.0700000003 132
 
1.1%
0.07999999821 69
 
0.6%
0.09000000358 27
 
0.2%
0.1099999994 34
 
0.3%
0.1199999973 31
 
0.3%
ValueCountFrequency (%)
13.39999962 3
 
< 0.1%
13.26000023 3
 
< 0.1%
13.13000011 3
 
< 0.1%
12.53999996 3
 
< 0.1%
12.43999958 3
 
< 0.1%
12.34000015 3
 
< 0.1%
11.64000034 3
 
< 0.1%
11.36999989 3
 
< 0.1%
10.43000031 3
 
< 0.1%
9.890000343 23
0.2%

ModeratelyActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73086168
Minimum0
Maximum6.48
Zeros4334
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.534680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.40000001
Q31
95-th percentile2.45
Maximum6.48
Range6.48
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0374368
Coefficient of variation (CV)1.4194708
Kurtosis7.8518188
Mean0.73086168
Median Absolute Deviation (MAD)0.40000001
Skewness2.5235152
Sum9024.68
Variance1.0762752
MonotonicityNot monotonic
2023-01-21T12:09:33.613137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4334
35.1%
0.400000006 169
 
1.4%
0.25 149
 
1.2%
0.7900000215 136
 
1.1%
0.1899999976 118
 
1.0%
0.4199999869 117
 
0.9%
0.5699999928 115
 
0.9%
0.6399999857 113
 
0.9%
0.2800000012 112
 
0.9%
0.349999994 110
 
0.9%
Other values (182) 6875
55.7%
ValueCountFrequency (%)
0 4334
35.1%
0.02999999933 33
 
0.3%
0.03999999911 36
 
0.3%
0.05000000075 15
 
0.1%
0.05999999866 7
 
0.1%
0.07999999821 23
 
0.2%
0.1099999994 28
 
0.2%
0.1199999973 32
 
0.3%
0.1400000006 26
 
0.2%
0.150000006 57
 
0.5%
ValueCountFrequency (%)
6.480000019 28
0.2%
6.210000038 27
0.2%
5.599999905 28
0.2%
5.400000095 28
0.2%
5.239999771 28
0.2%
5.119999886 27
0.2%
4.579999924 28
0.2%
4.559999943 28
0.2%
4.349999905 28
0.2%
4.21999979 56
0.5%

LightActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct412
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5320157
Minimum0
Maximum10.3
Zeros663
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.695117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.3499999
median3.54
Q34.8299999
95-th percentile6.46
Maximum10.3
Range10.3
Interquartile range (IQR)2.48

Descriptive statistics

Standard deviation1.8766462
Coefficient of variation (CV)0.53132441
Kurtosis-0.0325695
Mean3.5320157
Median Absolute Deviation (MAD)1.23
Skewness0.11776653
Sum43613.33
Variance3.5218008
MonotonicityNot monotonic
2023-01-21T12:09:33.768391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 663
 
5.4%
4.179999828 122
 
1.0%
4.710000038 114
 
0.9%
2.470000029 109
 
0.9%
2.670000076 106
 
0.9%
3.910000086 102
 
0.8%
4.5 93
 
0.8%
3.920000076 90
 
0.7%
3.769999981 90
 
0.7%
5.409999847 89
 
0.7%
Other values (402) 10770
87.2%
ValueCountFrequency (%)
0 663
5.4%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 26
 
0.2%
0.03999999911 8
 
0.1%
0.05999999866 3
 
< 0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 31
 
0.3%
ValueCountFrequency (%)
10.30000019 18
0.1%
9.479999542 18
0.1%
9.460000038 4
 
< 0.1%
8.970000267 27
0.2%
8.680000305 18
0.1%
8.409999847 18
0.1%
8.270000458 27
0.2%
8.260000229 2
 
< 0.1%
8.229999542 2
 
< 0.1%
7.949999809 2
 
< 0.1%

SedentaryActiveDistance
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00067946226
Minimum0
Maximum0.11
Zeros12212
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.827973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.11
Range0.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0078424973
Coefficient of variation (CV)11.542212
Kurtosis161.87581
Mean0.00067946226
Median Absolute Deviation (MAD)0
Skewness12.609561
Sum8.39
Variance6.1504764 × 10-5
MonotonicityNot monotonic
2023-01-21T12:09:33.876194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 12212
98.9%
0.009999999776 40
 
0.3%
0.1099999994 31
 
0.3%
0.1000000015 31
 
0.3%
0.01999999955 10
 
0.1%
0.05000000075 8
 
0.1%
0.0700000003 8
 
0.1%
0.03999999911 8
 
0.1%
ValueCountFrequency (%)
0 12212
98.9%
0.009999999776 40
 
0.3%
0.01999999955 10
 
0.1%
0.03999999911 8
 
0.1%
0.05000000075 8
 
0.1%
0.0700000003 8
 
0.1%
0.1000000015 31
 
0.3%
0.1099999994 31
 
0.3%
ValueCountFrequency (%)
0.1099999994 31
 
0.3%
0.1000000015 31
 
0.3%
0.0700000003 8
 
0.1%
0.05000000075 8
 
0.1%
0.03999999911 8
 
0.1%
0.01999999955 10
 
0.1%
0.009999999776 40
 
0.3%
0 12212
98.9%

VeryActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.935779
Minimum0
Maximum210
Zeros4658
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:33.941876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q336
95-th percentile97
Maximum210
Range210
Interquartile range (IQR)36

Descriptive statistics

Standard deviation34.884901
Coefficient of variation (CV)1.4574375
Kurtosis6.2872964
Mean23.935779
Median Absolute Deviation (MAD)8
Skewness2.2176195
Sum295559
Variance1216.9564
MonotonicityNot monotonic
2023-01-21T12:09:34.011395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4658
37.7%
1 370
 
3.0%
8 299
 
2.4%
19 236
 
1.9%
3 234
 
1.9%
6 206
 
1.7%
14 201
 
1.6%
4 181
 
1.5%
11 169
 
1.4%
30 169
 
1.4%
Other values (104) 5625
45.6%
ValueCountFrequency (%)
0 4658
37.7%
1 370
 
3.0%
2 135
 
1.1%
3 234
 
1.9%
4 181
 
1.5%
5 154
 
1.2%
6 206
 
1.7%
7 110
 
0.9%
8 299
 
2.4%
9 104
 
0.8%
ValueCountFrequency (%)
210 26
0.2%
207 26
0.2%
200 26
0.2%
194 26
0.2%
184 26
0.2%
137 31
0.3%
132 3
 
< 0.1%
129 3
 
< 0.1%
125 6
 
< 0.1%
123 31
0.3%

FairlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.340703
Minimum0
Maximum143
Zeros4319
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:34.083452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q324
95-th percentile65
Maximum143
Range143
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.037698
Coefficient of variation (CV)1.3285331
Kurtosis6.0326325
Mean17.340703
Median Absolute Deviation (MAD)10
Skewness2.2063656
Sum214123
Variance530.73554
MonotonicityNot monotonic
2023-01-21T12:09:34.154681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4319
35.0%
8 513
 
4.2%
16 410
 
3.3%
15 309
 
2.5%
10 290
 
2.3%
9 279
 
2.3%
13 260
 
2.1%
6 258
 
2.1%
14 242
 
2.0%
11 241
 
2.0%
Other values (68) 5227
42.3%
ValueCountFrequency (%)
0 4319
35.0%
1 68
 
0.6%
2 28
 
0.2%
3 65
 
0.5%
4 203
 
1.6%
5 158
 
1.3%
6 258
 
2.1%
7 160
 
1.3%
8 513
 
4.2%
9 279
 
2.3%
ValueCountFrequency (%)
143 28
 
0.2%
125 27
 
0.2%
122 28
 
0.2%
116 28
 
0.2%
115 28
 
0.2%
113 3
 
< 0.1%
98 28
 
0.2%
96 28
 
0.2%
95 111
0.9%
94 4
 
< 0.1%

LightlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct298
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.8488
Minimum0
Maximum518
Zeros660
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:34.228272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1144
median200
Q3258
95-th percentile352
Maximum518
Range518
Interquartile range (IQR)114

Descriptive statistics

Standard deviation97.403506
Coefficient of variation (CV)0.48738599
Kurtosis0.38865856
Mean199.8488
Median Absolute Deviation (MAD)58
Skewness0.026619009
Sum2467733
Variance9487.443
MonotonicityNot monotonic
2023-01-21T12:09:34.302006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 660
 
5.3%
214 169
 
1.4%
206 165
 
1.3%
153 161
 
1.3%
195 157
 
1.3%
141 155
 
1.3%
238 147
 
1.2%
194 136
 
1.1%
258 130
 
1.1%
139 128
 
1.0%
Other values (288) 10340
83.7%
ValueCountFrequency (%)
0 660
5.3%
1 9
 
0.1%
2 45
 
0.4%
3 58
 
0.5%
4 15
 
0.1%
9 46
 
0.4%
10 8
 
0.1%
11 3
 
< 0.1%
12 20
 
0.2%
15 3
 
< 0.1%
ValueCountFrequency (%)
518 18
0.1%
513 18
0.1%
512 18
0.1%
487 18
0.1%
480 18
0.1%
475 28
0.2%
461 18
0.1%
458 18
0.1%
439 27
0.2%
432 33
0.3%

SedentaryMinutes
Real number (ℝ)

Distinct451
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean799.39148
Minimum0
Maximum1440
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:34.374499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile472
Q1659
median734
Q3853
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)194

Descriptive statistics

Standard deviation267.21313
Coefficient of variation (CV)0.33427067
Kurtosis0.86026535
Mean799.39148
Median Absolute Deviation (MAD)100
Skewness0.77052215
Sum9870886
Variance71402.856
MonotonicityNot monotonic
2023-01-21T12:09:34.536687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 635
 
5.1%
692 167
 
1.4%
680 147
 
1.2%
709 144
 
1.2%
745 113
 
0.9%
621 112
 
0.9%
728 106
 
0.9%
676 102
 
0.8%
732 97
 
0.8%
712 89
 
0.7%
Other values (441) 10636
86.1%
ValueCountFrequency (%)
0 26
0.2%
2 15
0.1%
13 28
0.2%
48 15
0.1%
111 2
 
< 0.1%
125 18
0.1%
127 31
0.3%
218 3
 
< 0.1%
222 31
0.3%
241 27
0.2%
ValueCountFrequency (%)
1440 635
5.1%
1439 9
 
0.1%
1438 19
 
0.2%
1437 32
 
0.3%
1431 15
 
0.1%
1430 8
 
0.1%
1428 15
 
0.1%
1423 5
 
< 0.1%
1420 3
 
< 0.1%
1413 5
 
< 0.1%

Calories
Real number (ℝ)

Distinct576
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2322.8453
Minimum0
Maximum4900
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:34.611486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1401
Q11776
median2158
Q32859
95-th percentile3784
Maximum4900
Range4900
Interquartile range (IQR)1083

Descriptive statistics

Standard deviation760.08554
Coefficient of variation (CV)0.32722176
Kurtosis0.40276993
Mean2322.8453
Median Absolute Deviation (MAD)484
Skewness0.56285902
Sum28682494
Variance577730.03
MonotonicityNot monotonic
2023-01-21T12:09:34.685508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1688 135
 
1.1%
1980 104
 
0.8%
1819 102
 
0.8%
3061 81
 
0.7%
2361 77
 
0.6%
2194 74
 
0.6%
1496 72
 
0.6%
2066 67
 
0.5%
2105 67
 
0.5%
2086 62
 
0.5%
Other values (566) 11507
93.2%
ValueCountFrequency (%)
0 25
0.2%
52 28
0.2%
57 15
0.1%
120 2
 
< 0.1%
257 26
0.2%
403 15
0.1%
665 3
 
< 0.1%
741 31
0.3%
928 31
0.3%
1032 3
 
< 0.1%
ValueCountFrequency (%)
4900 18
0.1%
4552 26
0.2%
4546 26
0.2%
4501 26
0.2%
4392 26
0.2%
4274 26
0.2%
4236 31
0.3%
4163 31
0.3%
4157 31
0.3%
4092 31
0.3%

SleepDay
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size192.9 KiB
4/15/2016 12:00:00 AM
 
505
5/1/2016 12:00:00 AM
 
484
4/28/2016 12:00:00 AM
 
476
4/30/2016 12:00:00 AM
 
460
4/20/2016 12:00:00 AM
 
458
Other values (26)
9965 

Length

Max length21
Median length21
Mean length20.715015
Min length20

Characters and Unicode

Total characters255789
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/12/2016 12:00:00 AM
2nd row4/13/2016 12:00:00 AM
3rd row4/15/2016 12:00:00 AM
4th row4/16/2016 12:00:00 AM
5th row4/17/2016 12:00:00 AM

Common Values

ValueCountFrequency (%)
4/15/2016 12:00:00 AM 505
 
4.1%
5/1/2016 12:00:00 AM 484
 
3.9%
4/28/2016 12:00:00 AM 476
 
3.9%
4/30/2016 12:00:00 AM 460
 
3.7%
4/20/2016 12:00:00 AM 458
 
3.7%
4/21/2016 12:00:00 AM 447
 
3.6%
4/23/2016 12:00:00 AM 445
 
3.6%
4/29/2016 12:00:00 AM 444
 
3.6%
5/8/2016 12:00:00 AM 429
 
3.5%
4/16/2016 12:00:00 AM 424
 
3.4%
Other values (21) 7776
63.0%

Length

2023-01-21T12:09:34.749670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 12348
33.3%
am 12348
33.3%
4/15/2016 505
 
1.4%
5/1/2016 484
 
1.3%
4/28/2016 476
 
1.3%
4/30/2016 460
 
1.2%
4/20/2016 458
 
1.2%
4/21/2016 447
 
1.2%
4/23/2016 445
 
1.2%
4/29/2016 444
 
1.2%
Other values (23) 8629
23.3%

Most occurring characters

ValueCountFrequency (%)
0 63028
24.6%
2 30352
11.9%
1 30108
11.8%
/ 24696
 
9.7%
24696
 
9.7%
: 24696
 
9.7%
6 13559
 
5.3%
A 12348
 
4.8%
M 12348
 
4.8%
4 9005
 
3.5%
Other values (5) 10953
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 157005
61.4%
Other Punctuation 49392
 
19.3%
Space Separator 24696
 
9.7%
Uppercase Letter 24696
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63028
40.1%
2 30352
19.3%
1 30108
19.2%
6 13559
 
8.6%
4 9005
 
5.7%
5 5706
 
3.6%
3 1685
 
1.1%
9 1198
 
0.8%
8 1197
 
0.8%
7 1167
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 24696
50.0%
: 24696
50.0%
Uppercase Letter
ValueCountFrequency (%)
A 12348
50.0%
M 12348
50.0%
Space Separator
ValueCountFrequency (%)
24696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 231093
90.3%
Latin 24696
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63028
27.3%
2 30352
13.1%
1 30108
13.0%
/ 24696
 
10.7%
24696
 
10.7%
: 24696
 
10.7%
6 13559
 
5.9%
4 9005
 
3.9%
5 5706
 
2.5%
3 1685
 
0.7%
Other values (3) 3562
 
1.5%
Latin
ValueCountFrequency (%)
A 12348
50.0%
M 12348
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63028
24.6%
2 30352
11.9%
1 30108
11.8%
/ 24696
 
9.7%
24696
 
9.7%
: 24696
 
9.7%
6 13559
 
5.3%
A 12348
 
4.8%
M 12348
 
4.8%
4 9005
 
3.5%
Other values (5) 10953
 
4.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.9 KiB
1
10939 
2
1316 
3
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12348
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

Length

2023-01-21T12:09:34.803416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T12:09:34.858900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10939
88.6%
2 1316
 
10.7%
3 93
 
0.8%

TotalMinutesAsleep
Real number (ℝ)

Distinct256
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.10277
Minimum58
Maximum796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:34.918028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile166
Q1361
median432
Q3492
95-th percentile591
Maximum796
Range738
Interquartile range (IQR)131

Descriptive statistics

Standard deviation118.9441
Coefficient of variation (CV)0.28380652
Kurtosis1.522841
Mean419.10277
Median Absolute Deviation (MAD)66
Skewness-0.59288277
Sum5175081
Variance14147.699
MonotonicityNot monotonic
2023-01-21T12:09:34.988732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442 204
 
1.7%
441 155
 
1.3%
412 153
 
1.2%
388 124
 
1.0%
357 124
 
1.0%
322 124
 
1.0%
523 124
 
1.0%
354 123
 
1.0%
421 123
 
1.0%
531 122
 
1.0%
Other values (246) 10972
88.9%
ValueCountFrequency (%)
58 26
0.2%
59 31
0.3%
61 31
0.3%
62 31
0.3%
74 61
0.5%
77 31
0.3%
79 26
0.2%
82 31
0.3%
98 31
0.3%
99 31
0.3%
ValueCountFrequency (%)
796 30
0.2%
775 31
0.3%
750 31
0.3%
722 31
0.3%
700 31
0.3%
692 31
0.3%
681 31
0.3%
658 59
0.5%
651 31
0.3%
644 31
0.3%

TotalTimeInBed
Real number (ℝ)

Distinct242
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.19752
Minimum61
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.9 KiB
2023-01-21T12:09:35.067745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile179
Q1402
median462
Q3526
95-th percentile634
Maximum961
Range900
Interquartile range (IQR)124

Descriptive statistics

Standard deviation127.87062
Coefficient of variation (CV)0.27907313
Kurtosis3.3735023
Mean458.19752
Median Absolute Deviation (MAD)60
Skewness-0.19174502
Sum5657823
Variance16350.895
MonotonicityNot monotonic
2023-01-21T12:09:35.137435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
546 183
 
1.5%
458 154
 
1.2%
402 154
 
1.2%
510 151
 
1.2%
457 124
 
1.0%
501 124
 
1.0%
391 124
 
1.0%
961 123
 
1.0%
500 123
 
1.0%
543 123
 
1.0%
Other values (232) 10965
88.8%
ValueCountFrequency (%)
61 26
0.2%
65 62
0.5%
69 31
0.3%
75 31
0.3%
77 31
0.3%
78 30
0.2%
82 26
0.2%
85 31
0.3%
104 31
0.3%
107 31
0.3%
ValueCountFrequency (%)
961 123
1.0%
843 31
 
0.3%
775 31
 
0.3%
725 31
 
0.3%
722 31
 
0.3%
712 31
 
0.3%
704 31
 
0.3%
698 28
 
0.2%
689 31
 
0.3%
686 62
0.5%

Interactions

2023-01-21T12:09:31.201436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:14.541382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.882179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.884724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.010772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.139650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.159476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.275270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.296947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.440995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.531583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.648595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.724432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.847795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.918387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.034978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.265966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:14.612173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-01-21T12:09:18.077679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.203228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.225746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.340469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.360875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.507695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.602425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.718976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.790583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.916196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.983608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.103541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.324740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:14.675778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.013667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.011946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.138262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.262031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.286428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.399988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.420111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.578988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.662990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.782229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.852134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.978713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.044689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.167933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.390660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:14.745984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.080950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.168150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.204577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.327071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.352741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.466217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.488191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.656913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.731569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.850451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.918589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.048191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.203268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.237373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.457316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:14.819252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.145171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.233806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.271441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.392323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.418676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.533866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.554889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.729881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.800615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.920839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.985353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.118244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.268420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.305881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.519266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.029025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.206653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.296750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.334320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.454985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.482424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.599093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.618542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.800973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.863372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.985334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.049774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.184926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.330656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.371742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.585568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.096447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.268975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.362278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.401089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.520088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.547961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.665981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.689330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.869521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.929559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.054367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.207213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.252621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.395432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.440535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.649867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.164162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.331200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.431158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.468809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.584225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.612293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.732691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.758252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.935345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.994987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.121786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.270860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.319711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.459294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.507751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.712251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.229256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.391895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.494834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.532242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.652612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.674410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-01-21T12:09:28.585385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.714075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.774150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.965544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.599145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.638734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.750288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.789743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.910393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.928991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.046269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.187434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.262395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.395504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.454469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.589770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.652286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.775747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.840563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:32.030525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.675227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.702090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.816268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.857152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:19.975077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.086760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.110955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.255062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.330968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.461156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.523107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.656000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.721000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.844010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.909966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:32.091071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.743411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.763020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.880718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.918934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.034495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.147883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.171625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.318576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.394352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.523331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.588697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.718415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.784673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.906762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:30.976373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:32.157603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:15.817845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:16.827283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:17.948066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:18.986794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:20.098743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:21.213875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:22.237069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:23.382904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:24.463028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:25.589528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:26.659805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:27.787028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:28.854250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:29.973005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T12:09:31.045728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-21T12:09:35.210160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IdTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesTotalMinutesAsleepTotalTimeInBedActivityDateSleepDayTotalSleepRecords
Id1.000-0.0280.0270.0260.3160.1500.008-0.070-0.0180.2130.023-0.207-0.0390.4260.1220.0060.0000.1480.166
TotalSteps-0.0281.0000.9840.9840.1430.7640.7660.6360.0310.7130.7340.473-0.2700.403-0.130-0.0820.2400.0300.052
TotalDistance0.0270.9841.0001.0000.1940.7660.7630.6520.0430.7220.7300.447-0.2570.500-0.125-0.0830.2480.0350.066
TrackerDistance0.0260.9841.0001.0000.1860.7650.7620.6520.0280.7210.7290.447-0.2570.500-0.125-0.0830.2490.0360.066
LoggedActivitiesDistance0.3160.1430.1940.1861.0000.2320.1210.1300.2140.3330.071-0.0670.0010.2980.0250.0200.2040.0390.020
VeryActiveDistance0.1500.7640.7660.7650.2321.0000.7190.2110.0410.9730.7230.022-0.0980.437-0.154-0.1390.2070.0500.030
ModeratelyActiveDistance0.0080.7660.7630.7620.1210.7191.0000.3570.0430.7010.9700.179-0.1620.323-0.191-0.1090.2550.0000.020
LightActiveDistance-0.0700.6360.6520.6520.1300.2110.3571.0000.0040.2080.3050.854-0.3250.4210.019-0.0010.2460.0320.053
SedentaryActiveDistance-0.0180.0310.0430.0280.2140.0410.0430.0041.0000.0250.0210.0120.0260.044-0.027-0.0390.1590.0000.011
VeryActiveMinutes0.2130.7130.7220.7210.3330.9730.7010.2080.0251.0000.722-0.004-0.0840.501-0.129-0.1240.2460.0000.028
FairlyActiveMinutes0.0230.7340.7300.7290.0710.7230.9700.3050.0210.7221.0000.142-0.1550.360-0.186-0.1090.2580.0000.034
LightlyActiveMinutes-0.2070.4730.4470.447-0.0670.0220.1790.8540.012-0.0040.1421.000-0.3930.1930.0550.0270.2360.0330.071
SedentaryMinutes-0.039-0.270-0.257-0.2570.001-0.098-0.162-0.3250.026-0.084-0.155-0.3931.000-0.016-0.145-0.1730.2870.0460.062
Calories0.4260.4030.5000.5000.2980.4370.3230.4210.0440.5010.3600.193-0.0161.0000.026-0.0950.2760.0000.068
TotalMinutesAsleep0.122-0.130-0.125-0.1250.025-0.154-0.1910.019-0.027-0.129-0.1860.055-0.1450.0261.0000.9180.0000.2540.298
TotalTimeInBed0.006-0.082-0.083-0.0830.020-0.139-0.109-0.001-0.039-0.124-0.1090.027-0.173-0.0950.9181.0000.0000.2650.248
ActivityDate0.0000.2400.2480.2490.2040.2070.2550.2460.1590.2460.2580.2360.2870.2760.0000.0001.0000.0000.000
SleepDay0.1480.0300.0350.0360.0390.0500.0000.0320.0000.0000.0000.0330.0460.0000.2540.2650.0001.0000.314
TotalSleepRecords0.1660.0520.0660.0660.0200.0300.0200.0530.0110.0280.0340.0710.0620.0680.2980.2480.0000.3141.000

Missing values

2023-01-21T12:09:32.254593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T12:09:32.407610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesSleepDayTotalSleepRecordsTotalMinutesAsleepTotalTimeInBed
015039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/12/2016 12:00:00 AM1327346
115039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/13/2016 12:00:00 AM2384407
215039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/15/2016 12:00:00 AM1412442
315039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/16/2016 12:00:00 AM2340367
415039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/17/2016 12:00:00 AM1700712
515039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/19/2016 12:00:00 AM1304320
615039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/20/2016 12:00:00 AM1360377
715039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/21/2016 12:00:00 AM1325364
815039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/23/2016 12:00:00 AM1361384
915039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/24/2016 12:00:00 AM1430449
IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesSleepDayTotalSleepRecordsTotalMinutesAsleepTotalTimeInBed
1243187920096655/10/201600.00.00.00.00.00.00.000048574/22/2016 12:00:00 AM1391407
1243287920096655/10/201600.00.00.00.00.00.00.000048574/23/2016 12:00:00 AM1339360
1243387920096655/10/201600.00.00.00.00.00.00.000048574/27/2016 12:00:00 AM1423428
1243487920096655/10/201600.00.00.00.00.00.00.000048574/28/2016 12:00:00 AM1402416
1243587920096655/10/201600.00.00.00.00.00.00.000048574/29/2016 12:00:00 AM1398406
1243687920096655/10/201600.00.00.00.00.00.00.000048574/30/2016 12:00:00 AM1343360
1243787920096655/10/201600.00.00.00.00.00.00.000048575/1/2016 12:00:00 AM1503527
1243887920096655/10/201600.00.00.00.00.00.00.000048575/2/2016 12:00:00 AM1415423
1243987920096655/10/201600.00.00.00.00.00.00.000048575/3/2016 12:00:00 AM1516545
1244087920096655/10/201600.00.00.00.00.00.00.000048575/4/2016 12:00:00 AM1439463